Rank priors for continuous non-linear dimensionality reduction
Author(s)Darrell, Trevor J.; Urtasun, Raquel; Geiger, Andreas
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Discovering the underlying low-dimensional latent structure in high-dimensional perceptual observations (e.g., images, video) can, in many cases, greatly improve performance in recognition and tracking. However, non-linear dimensionality reduction methods are often susceptible to local minima and perform poorly when initialized far from the global optimum, even when the intrinsic dimensionality is known a priori. In this work we introduce a prior over the dimensionality of the latent space that penalizes high dimensional spaces, and simultaneously optimize both the latent space and its intrinsic dimensionality in a continuous fashion. Ad-hoc initialization schemes are unnecessary with our approach; we initialize the latent space to the observation space and automatically infer the latent dimensionality. We report results applying our prior to various probabilistic non-linear dimensionality reduction tasks, and show that our method can outperform graph-based dimensionality reduction techniques as well as previously suggested initialization strategies. We demonstrate the effectiveness of our approach when tracking and classifying human motion.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Institute of Electrical and Electronics Engineers
Geiger, A., R. Urtasun, and T. Darrell. “Rank priors for continuous non-linear dimensionality reduction.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 880-887. © 2009 Institute of Electrical and Electronics Engineers.
Final published version
INSPEC Accession Number: 10835871